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An efficient method to globally identify nonlinearly interacting inputs to biological systems

Author: Brian Fulton-Howard; Icahn School of Medicine at Mount Sinai. Graduate School of Biomedical Sciences. Neuroscience Doctoral Program.
Publisher: New York, N.Y. : Icahn School of Medicine at Mount Sinai, ©2017.
Dissertation: Thesis (Ph. D.) Icahn School of Medicine at Mount Sinai 2017
This dissertation is also available on the World Wide Web. Access is restricted to computers located within Mount Sinai or to those users eligible for remote access services.
Edition/Format:   Thesis/dissertation : Thesis/dissertation : Manuscript   Archival Material : English
Summary:
Finding interactions between inputs to complex biological systems is a question of major interest and great challenges. Biological inputs can often behave differently when combined with other inputs than alone because of biological complexity. The complexity of organisms arises from multiple overlapping mechanisms for interacting biological processes, and complex amplification, feedback and feedforward effects. This  Read more...
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Genre/Form: Academic Dissertation
Academic theses
Material Type: Thesis/dissertation, Manuscript
Document Type: Book, Archival Material
All Authors / Contributors: Brian Fulton-Howard; Icahn School of Medicine at Mount Sinai. Graduate School of Biomedical Sciences. Neuroscience Doctoral Program.
OCLC Number: 1011508692
Description: xi, 89 pages : illustrations (chiefly color), charts, tables ; 28 cm
Responsibility: Brian Fulton-Howard.

Abstract:

Finding interactions between inputs to complex biological systems is a question of major interest and great challenges. Biological inputs can often behave differently when combined with other inputs than alone because of biological complexity. The complexity of organisms arises from multiple overlapping mechanisms for interacting biological processes, and complex amplification, feedback and feedforward effects. This leads to complex interactions between stimuli, neurotransmitters, hormones and drugs. Understanding the interactions between natural inputs like endogenous compounds can help us understand normal and diseased biological mechanisms, and understanding interactions between drugs can lead to greater safety and efficacy with the use of multiple drugs. Various theoretical methods exist to predict interactions but they are biased by current knowledge. Methods to experimentally find interactions are handicapped by combinatorial complexity or do not take important dose dependent effects into account. Here we present Combinatorial Reduction of Interacting Tuple Tests (CRITT), an unbiased experimental method to identify biological nonlinearity. CRITT uses a heuristic to pool inputs at multiple doses, then uses algorithms to automatically deconvolve the pools and identify nonlinear pairs. We also demonstrate the efficacy of CRITT on a model of the Guinea Pig ventricular myocyte. CRITT can be expected to reduce the number of required experiments by at least two thirds.

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